Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter Published on: HotPower ‘09 Presentation By: Liang Hao Tuesday, August 03, 2010

Author Christopher Stewart from The Ohio State University Kai Shen from University of Rochester Publications: Of Christopher Stewart Of Kai Shen

Motivation To reduce datacenters’ dependence on costly and less clean energy from the grid Hence to maximize the use of renewable energies To explore the possibility of evaluating the request-level power consumption

Problems Datacenters powered by renewable energy need backups Applications in the datacenter must be available 24x7 But wind and solar energy are intermittent Datacenters powered by renewable energy need backups Primary options: Grid, generator, battery Alternatives are either dirty and/or costly Renewables are precious! renewable = joule converted from solar/wind The preferred energy source Available only sometimes and costly to store

Opportunities Capacity planning Load balancing Compute power should fluctuate with intermittent outages—i.e., turn machines off Load balancing Route requests to datacenters with unused renewables Migrate services to datacenters with renewables

Intermittency 1.Datacenter Modeling

Intermittency 1.Datacenter Modeling Automatic transfer switch (ATS) Input 2 power sources, outputs 1 power source Monitors power from the primary source When power from primary dips below threshold, ATS switches to secondary When primary exhibits power of threshold, ATS switches back to secondary

Intermittency 1.Datacenter Modeling Key parameters related to the ATS to ensure dependability or reliability, threshold equals peak consumption to make full use of renewable energies, scale down the threshold

Intermittency 2.Wind Intermittency Battery backup too costly But if we apply renewable-aware management, there is enough supply from other datacenters

Intermittency 3.Renewable Utility when threshold set to peak power, the utility of wind turbine power production drops 65% compared to when threshold set to zero.

Intermittency 3.Renewable Utility Economical feasibility (metric: cost per KW-hour) Average price for commercial electricity $0.10 KW-hour $2.4M to erect a wind turbine that is connected (directly) to a datacenter [European Wind Energy Assc.] $1.6M installation 2% annual maintenance fees Lifetime of turbine: 20 years Datacenter at CA or MT could use 24M KWh Either high power consumption or zero threshold Wind-powered datacenter in MT: $0.04 KWh

Request-level Event Profiling To estimate the power consumption of individual requests With quantized statistics, scheduler could possibly route some requests to datacenters with unused renewables

Request-level Event Profiling Tracing the route that a request go through, including CPU usage and other hardware events. We configured the performance counters to assemble three predictor metrics for our power model: L2 cache requests per CPU cycle (Ccache), memory transactions per CPU cycle (Cmem), and the ratio of non-halt CPU cycles (Cnonhalt).

Request-level Event Profiling Power consumption is calculated according to the expression below where P’s are coefficient parameters for the linear model. are constants that approximate ceiling values for the predictor metrics.

Request-level Event Profiling micro benchmarks 1) idle 2) CPU spinning with no access to cache or memory 3/4) Apache web server with either short requests (no more than 1KB files) or long requests (files of 100 KB– 1 MB) 5/6) OpenSSL RSA encryption/decryption using either a small key or a large key. We also use four full server workloads 7) TPC-C running on the MySQL database 8) TPC-H running on the MySQL database 9) RUBiS 10) WeBWorK .

Request-level Event Profiling Request workloads executed in isolation WattsUp power meter measures watts and joules Processor was not adjusted during tests

Vision

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